Abstract
As mental health content on platforms like TikTok increases steeply, it is important for us to characterize and understand how it is shared. Unfortunately, there are no precise mechanisms for identifying different types of mental health content or for users to indicate content preferences. Expanding on prior work and a qualitative typology we discovered, we present a preliminary exploration of features from 169 hand-labeled videos from a dataset of 19,000+ videos related to clinical and pragmatic mental health content. Our findings provide opportunities for future advancements in moderating mental health content and personalizing users' interactions.
Original language | English (US) |
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Title of host publication | CSCW 2023 Companion - Conference Companion Publication of the 2023 Computer Supported Cooperative Work and Social Computing |
Editors | Morgan Ames, Susan Fussell, Eric Gilbert, Vera Liao, Xiaojuan Ma, Xinru Page, Mark Rouncefield, Vivek Singh, Pamela Wisniewski |
Publisher | Association for Computing Machinery |
Pages | 149-153 |
Number of pages | 5 |
ISBN (Electronic) | 9798400701290 |
DOIs | |
State | Published - Oct 14 2023 |
Event | 26th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2023 - Minneapolis, United States Duration: Oct 14 2023 → Oct 18 2023 |
Publication series
Name | Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW |
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Conference
Conference | 26th ACM Conference on Computer-Supported Cooperative Work and Social Computing, CSCW 2023 |
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Country/Territory | United States |
City | Minneapolis |
Period | 10/14/23 → 10/18/23 |
Bibliographical note
Publisher Copyright:© 2023 ACM.
Keywords
- Data Analysis
- Mental Health
- TikTok